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arXiv · 2409.02335

What Do You See in Common? Learning Hierarchical Prototypes over Tree-of-Life to Discover Evolutionary Traits

Harish Babu Manogaran·M. Maruf·Arka Daw·Kazi Sajeed Mehrab·Caleb Patrick Charpentier·Josef C. Uyeda·Wasila Dahdul·Matthew J Thompson·Elizabeth G Campolongo·Kaiya L Provost·Wei-Lun Chao·Tanya Berger-Wolf·Paula M. Mabee·Hilmar Lapp·Anuj Karpatne
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Abstract

A grand challenge in biology is to discover evolutionary traits - features of organisms common to a group of species with a shared ancestor in the tree of life (also referred to as phylogenetic tree). With the growing availability of image repositories in biology, there is a tremendous opportunity to discover evolutionary traits directly from images in the form of a hierarchy of prototypes. However, current prototype-based methods are mostly designed to operate over a flat structure of classes and face several challenges in discovering hierarchical prototypes, including the issue of learning over-specific prototypes at internal nodes. To overcome these challenges, we introduce the framework of Hierarchy aligned Commonality through Prototypical Networks (HComP-Net). The key novelties in HComP-Net include a novel over-specificity loss to avoid learning over-specific prototypes, a novel discriminative loss to ensure prototypes at an internal node are absent in the contrasting set of species with different ancestry, and a novel masking module to allow for the exclusion of over-specific prototypes at higher levels of the tree without hampering classification performance. We empirically show that HComP-Net learns prototypes that are accurate, semantically consistent, and generalizable to unseen species in comparison to baselines.

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Harish Babu Manogaran, M. Maruf, Arka Daw, Kazi Sajeed Mehrab, Caleb Patrick Charpentier, Josef C. Uyeda, Wasila Dahdul, Matthew J Thompson, Elizabeth G Campolongo, Kaiya L Provost, Wei-Lun Chao, Tanya Berger-Wolf, Paula M. Mabee, Hilmar Lapp, Anuj Karpatne. 2024-09-03. What Do You See in Common? Learning Hierarchical Prototypes over Tree-of-Life to Discover Evolutionary Traits. https://arxiv.org/abs/2409.02335

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